MAES: A Multi-Agent Systems Framework for Embedded Systems
C. Chan Zheng (TU Delft - Electrical Engineering, Mathematics and Computer Science)
K.G. Langendoen – Mentor
Johan Carvajal Godínez – Graduation committee member
A. Menicucci – Graduation committee member
Matthijs T. J. Spaan – Graduation committee member
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Miniaturization and cost reduction of hardware components have created a trend in the space industry where the traditional centralized computer is being replaced by distributed computer architecture. However, this trend comes with a cost: the on-board software complexity of the space missions has increased. The complexity has origins in the requirements of the missions where in general, these are coordination and control-related processes. As the coordination and the control of the satellite's activities are not trivial tasks, the Multi-Agent Systems(MAS)-approach has been proposed as a new architectural style due to its distributed nature. There are several existing frameworks for implementing MAS-based applications, however, most of them are neither designed to satisfy real-time requirements nor designed to be implemented in highly-constrained embedded systems. Therefore, the purpose of this thesis is to develop a new tool for MAS-based applications: A Multi-Agent Framework for Embedded Systems (MAES).
The framework was implemented on top of a Real-Time Operating System: TI-RTOS, therefore, applications implemented with MAES have realtime characteristics. Experiments have shown that the execution time of an Attitude Determination algorithm is consistent on each call with a variance value of the order of 10^5 [s^2], demonstrating the predictability of the framework. Furthermore, the user coding effort is reduced as several routines are standardized and encapsulated into MAES' API. However, the predictability and ease-of-use come with a slight cost: experiments have shown that MAES-based applications lead to an increase of 6.7 KB in average in Flash memory and 4.5 KB in average in SRAM memory with respect to its non-agent implementation. Also, the CPU utilization increases as inter-agent communication requires additional processing time, also increasing the power consumption. However, the increase is low as the results have shown that is less than 1% in average.